To this end, we evaluate our approach using data from the KITTI Vision Benchmark [1]}, where we use the provided point clouds generated by a Velodyne HDL-64E S2 recorded at a rate of 10 Hz.
To evaluate the performance of odometry, the dataset proposes to compute relative errors in respect to translation and rotation averaged over different distances between poses and averaging it.
The ground truth poses are generated using pose information from an inertial navigation system and in most sequences the GPS position is referenced to a base station, which makes it quite accurate, but still often only locally consistent.
